class_docstr: Class for Hugging Face sequence classification models trained on a NLI dataset
  
  Attributes:
      model_path: path to HF model
      tokenizer_path: path to HF tokenizer
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      model_class: Class used to initialise model
      tokenizer_class: Class used to initialise tokenizer
      device: Device for model. Defaults to "cuda" if available.
docstr: Classifies text, given a set of labels.
docstr_tok: ["Classifies","text",",","given","a","set","of","labels","."]
code: def classify(self, text, labels, top_k):
      """
      Classifies text, given a set of labels.
      """
      probabilities = []
      for text, labels in zip(text, labels):
          probs = {}
          probs_list = self.calculate_probability(text, labels)
  
          for prob, label in zip(probs_list, labels):
              probs[label] = prob
  
          probs = sorted(
              probs.items(), key=lambda x: x[1], reverse=True
          )
          probs = {k: v for k, v in probs[:top_k]}
  
          probabilities.append(probs)
  
      return probabilities
  
file_path: backprop/backprop/models/hf_nli_model/model.py
imports: from typing import List, Dict
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from backprop.models import HFModel
  import torch
label_desc: Use image node_id 5 for calling the HFNLIModel obj's underlying member method code with example usage: obj.classify(text, labels, top_k) and returns: probabilities